다주파수 생체임피던스 저항을 이용한 당뇨병 환자의 허증 변증 예측

• Published in: 동의생리병리학회지 Vol. 36; no. 3; pp. 94 - 99
• Main Authors: 김가혜(Kahye Kim), 김슬기(Seul Gee Kim), 차지윤(Jiyun Cha), 유호룡(Ho-Ryong Yoo), 김재욱(Jaeuk U. Kim)
• Format: Journal Article
• Language: Korean
• Published: 한의병리학회 2022
• Subjects: 한의학; Resistance; Diabetes; Multi-frequency Bioimpedance; Pattern Identification; Qi-Blood-Yin-Yang Pattern
• ISSN: 1738-7698; 2288-2529; 2283-2529
• DOI: 10.15188/kjopp.2022.06.36.3.94

The discovery of biomarkers related to pattern identification (PI), the core diagnostic theory of Korean medicine (KM), is one of the methods that can provide objective and reliable evidence by applying PI to clinical practice. In this study, 40 diabetic patients and 41 healthy control subjects recruited from the Korean medicine clinic were examined to determine the human electrical response related to the deficiency pattern, a representative pattern of diabetes. Qi-Blood-Yin-Yang deficiency pattern scores, which are representative deficiency patterns for diabetes mellitus, were obtained through a questionnaire with verified reliability and validity, and the human electrical response was measured non-invasively using a bioimpedance meter. In ANCOVA analysis using gender as a covariate, the 5 kHz frequency resistance and 5-250 kHz frequency reactance were significantly lower in the diabetic group than in non-diabetic control group. In addition, the multiple regression analysis showed a positive correlation (R2=0.11~0.19) between the Yang deficiency pattern score and resistance value for the diabetic group; the correlation was higher at higher frequencies of 50kHz (R2=0.18) and 250kHz (R2=0.19) compared to 5kHz(R2=0.11). In contrast, there was no such significant association in the control group. It implies that bioimpedance resistance measured at finite frequencies may be useful in predicting Yang deficiency, which is closely related to diabetic complications by reflecting the decrease in body water content and metabolism. In the future, large-scale planned clinical studies will be needed to identify biomarkers associated with different types of PI in diabetes.